) -> "Qdrant":
        return cast(
            Qdrant,
            super().from_documents(
                documents,
                embedding,
                url=url,
                port=port,
                grpc_port=grpc_port,
                prefer_grpc=prefer_grpc,
                https=https,
                api_key=api_key,
                prefix=prefix,
                timeout=timeout,
                host=host,
                collection_name=collection_name,
                distance_func=distance_func,
                content_payload_key=content_payload_key,
                metadata_payload_key=metadata_payload_key,
                **kwargs,
            ),
        )
[docs]    @classmethod
    def from_texts(
        cls,
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        url: Optional[str] = None,
        port: Optional[int] = 6333,
        grpc_port: int = 6334,
        prefer_grpc: bool = False,
        https: Optional[bool] = None,
        api_key: Optional[str] = None,
        prefix: Optional[str] = None,
        timeout: Optional[float] = None,
        host: Optional[str] = None,
        collection_name: Optional[str] = None,
        distance_func: str = "Cosine",
        content_payload_key: str = CONTENT_KEY,
        metadata_payload_key: str = METADATA_KEY,
        **kwargs: Any,
    ) -> "Qdrant":
        """Construct Qdrant wrapper from raw documents.
        Args:
            texts: A list of texts to be indexed in Qdrant.
            embedding: A subclass of `Embeddings`, responsible for text vectorization.
            metadatas: